An Electrochemical Biosensor for Detection of Thrombin based on Displacement Mode
Bibliographic record
Abstract
A sensitive electrochemical biosensor for detection of thrombin based on target protein-induced strand displacement is presented. For this proposed biosensor, dsDNA which was prepared by the hybridization reaction of the immobilized probe ssDNA (IP) containing thiol group and thrombin aptamer base sequence was initially immobilized on the Au electrode by self-assemble via Au-S bind, and a DNA labeled with PdS nanoparticles (DP-PdS) was used as a detection probe. When the so prepared dsDNA modified Au electrode was immersed into a solution containing target protein and DP-PdS, the aptamer in the dsDNA give priority to to form G-quarter structure with the present target protein and the dsDNA sequence was released one single strand and returned to IP strand which consequently hybridized with DP-PdS. After dissolving the captured PdS particles from the electrode, a mercury-film electrode was used for electrochemical detection of these Pd2+ ions which offered sensitive electrochemical signal transduction. The peak current of Pd2+ ions had a good linear relationship with the thrombin concentration in the range of 7.3×10−8–7.3×10-11 mol/L and the detection limit was 2.3×10-11 mol/L of thrombin. The detection was also specific for thrombin without being affected by the coexistence of other proteins, such as BSA and lysozyme.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".